/*
The analyses are organized for the specific results discussed
in the section titled: 

How much does ostensible measurement precision help?
*/

use "../Metadata/tess_analysisdata.dta", clear

** Comparing single measures to multi-item scales

* number of items using a single measure instead of multi-item scale - 74.8%
tab outcome_multimeasure if insample == 1

* multiple measures did not increase the likleihood of hypothesis being supported - 22.0% vs. 24.0%
prtest hyp_true if insample == 1, by(outcome_multimeasure)

* studies with scales less often had positive results -- 45.4% vs. 60.7%
preserve
collapse (max) outcome_multimeasure successfulexp_insample, by(proposal_id)
prtest successfulexp_insample, by(outcome_multimeasure)
restore

* single-item tests had smaller sample sizes - 833 vs. 1010
table outcome_multimeasure if insample == 1, stat(median N_person)


** Comparing binary outcomes to multiple outcome categories

cap drop binoutcome
gen binoutcome = outcome_categories == 2 if outcome_categories < .

* hypotheses using binary outcomes more likely to be supported -- 43.4% vs. 20.3%
prtest hyp_true if insample == 1, by(binoutcome)

	* pattern not explained by conjoint/dce studies -- 40.2% vs. 20.4%
	prtest hyp_true if insample == 1 & type_conj_dce == 0, by(binoutcome)

* median sample sizes smaller for tests with binary outcome -- 724 vs. 941
table binoutcome if insample == 1, stat(median N_person)
